Given the many layers of assumptions and allocations involved in modeling the emissions of the complex advertising supply chain, it is critical to understand the accuracy of emissions data both for assessing provider quality and as a gating factor to incorporate data into corporate and regulatory reporting.

Data Quality Components

Each data element included in an emissions model should include:

  • Grid mix data quality (1-5)
  • Organization data quality (1-5)
  • Property data quality (1-5)
  • Ad stack data quality (1-5)
  • Average ad platform data quality (1-5)
  • Ad format data quality (1-5)

In addition, it’s important that the provided activity data has sufficient granularity for accurate modeling:

  • Input granularity score (1-5)

Grid mix

Grid intensity can fluctuate significantly on an hourly basis due to the variable nature of both renewable energy sources (sun, wind) and electricity demand. To achieve effective decarbonization, having hourly data is critical. However, this isn’t broadly available in many countries.

Data source and timescaleData Quality
Country, monthly or annual2
Country, daily3
Country + region, daily4
Country + region, hourly5


Criteria for accurate sustainability data at the organization level:

  • An organization should provide a sustainability report that details its full carbon footprint, including scopes 1, 2, and all scope 3 categories. This report should clearly detail methodology for each category.

  • Any adjustments to the calculations for RECs, PPAs, offsets, or carbon credits should detail exactly what was purchased, from whom, and on what timeframe. Location-based scope 2 data should always be provided.

  • A sustainability report should be published within 6 months of the end of the previous calendar year to be considered for sustainability purposes.

Report elementData Quality
Scope 1 and 2, market-based+1
Location-based scope 2+1
All scope 3 categories provided+1
Lines of business broken out+1

Ad Platform

An ad platform should provide 1) requests received, 2) requests sent (traffic shaping), and 3) emissions data from datacenters or cloud providers. The ad platform score starts at 1 and increments based on the presence of various data elements.

Ad platform data elementData Quality
Server emissions and request data+1
Traffic shaping data+1
All data provided monthly+1
All data provided regionally+1

Example: A company provides annual server emissions, requests received, and traffic shaping data. This would be data quality 3.

The average ad platform data quality is the average score of all direct ad platforms that the property works with.


Web, App, Social, CTV and Digital Audio properties

For web, app, social, CTV and digital audio media properties, the three key metrics include session time, session weight, and ad load. These metrics can be extrapolated from other publicly shared metrics (such as Monthly Active Users, Revenue, etc.), or estimated by 3rd party tools that employ panels (such as SimilarWeb). They can also be observed through crawling and scraping, or measured directly by the publisher through tools like Google Analytics.

Property MetricsExample Data SourceData Quality
Not availableN/a1
Extrapolated from other metricsYearly Financial Report2
3rd party estimatedPanels3
ObservedControlled Crawling or Scraping4
1st party measuredPublisher Analytics5

Each key metric’ data quality is evaluated separately, then the property’ data quality score is the lower closest integer (floor) of the sum of each key metric’ data quality score divided by 3.


MetricExample Data SourceData Quality
Session timeSimilarWeb3
Session weightSimilarWeb3
Ad loadPublisher’ Google Ad Manager account5

Property data quality score: (3+3+5) / 3 = 3.67 ⇒ 3

DOOH screens

For DOOH screens, the key metric is power draw. It can be acquired on a per screen basis, for example using a power metre, or can be estimated using using screen size or manufacturer specifications.

Property MetricsGranularityData Quality
Not availableN/a1
Extrapolated based on physical dimensions and venue typeN/a2
Derived from manufacturer’ specifications (typical draw)N/a3
Observed from energy bills or measured through metresMonthly or Yearly4
Observed from energy bills or measured through metresHourly5

Ad Stack

The accuracy of the ad stack used by the publisher for the placement depends on accurate representation of all direct and indirect ad platforms. The ad stack score starts at 1 and increments based on provided data components.

Ad Stack ComponentData Quality
Ad stack mapped via observed data+1
Ads.txt validated+1
Ad platforms mapped to region, device, and format+1
Placements mapped to GPID and ad platform+1

Ad Format

The ad format data should include all of elements included upon the initial render of the ad format. Advertiser-provided assets should be identified so that they can be replaced with actual data during the measurement process. All ad platforms that are part of the rendering process should be included, especially ad servers, real-time measurement providers, and video players.

Ad Format data elementData Quality
Technical specs provided+1
Media assets identified+1
All static assets measured and included+1
Video player is identified and has data quality of at least 3+1

Input granularity

Provided inputs must match the underlying model in order to provide accurate output. For instance, if the user provides “ANZ” as a country, that might not match “AU” or “NZ”, causing a mapping issue that could cause a fallback to using worldwide grid mix and a significant loss of accuracy.

Granularity % is the percentage of recommended input fields that are provided and match valid values.

RegionFor countries with multiple grid regions, eg US, CA, AU
Device Type
TimeAt least hourly granularity
Ad format
Creative asset weights

Input granularity to data quality map

Granularity %Data Quality
< 30%1
30% - 50%2
50% - 70%3
70% - 90%4
90% - 100%5

Input granularity example

FieldProvided InputMatch
SellerGoogle AdXno
Device TypePhoneyes
Ad format320x50 banneryes
Creative weight(omitted)no

Input granularity percentage: 6/12 = 50%

Input granularity score: 2